Portable scanning device for ascertaining attributes of sample materials

ABSTRACT

The disclosed embodiments include at least one system, method, and device for ascertaining attributes of sample material. A method includes enabling a scanning device to capture images of samples placed on an internal surface of the scanning device. The captured images can be communicated over a communications network to a service (e.g., cloud-based service) that can detect features in the captured images in accordance with artificial intelligence techniques to determine attributes of a sample material such as authenticity, purity, or quality. The attributes of the sample material can be certified and communicated for display on a computer, mobile device (e.g., phone, tablet) or scanning device or with interested third-parties.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. provisional patent applicationSer. No. 62/715,736, filed on Aug. 7, 2018 and titled “TECHNOLOGY FORASCERTAINING THE QUALITY AND AUTHENTICITY OF PLANT MATERIALS,” which isincorporated herein in its entirety by this reference thereto.

TECHNICAL FIELD

The disclosed teachings relate to a standalone or distributed systemincluding a portable scanning device that can capture images of samplematerials and reliably ascertain attributes of the sample materialsbased on image data.

BACKGROUND

Biological materials are sourced and used throughout developed anddeveloping countries for consumption, as home remedies, and in drugproducts. Examples include plant materials such as herbs (e.g., parsley)and spices (e.g., cinnamon) and animal materials such as meat and fish.Accordingly, there is a large and distributed global market for certainmaterials. The materials are typically pre-processed at their sourcesbefore distribution to suppliers, manufacturers, retailers, orconsumers. For example, plant and animal material may be fresh, dried,cooked, whole, chopped, minced, ground, etc.

Attributes such as the color, size, shape and texture of the materialmay vary depending upon the genetics of the individual species and thenatural conditions when and where the plant material is grown or animalis raised. Examples include the geographic region for cultivation, soilcomposition, water quality, weather conditions including temperature andhumidity, sunshine intensity, and growth period. In another example,material may be contaminated with hazardous impurities such as animalfecal matter, contaminants such as rocks and sticks, or adulterated withfillers such as rice or soy powder.

As a result, intermediaries in a distribution chain of commerce seek toascertain the attributes (e.g., authenticity, purity, quality) ofmaterials. In conventional techniques, the attributes of materials aresubjectively judged based on a skilled person's experience by observingthe shape and color of a sample, in smelling flavor and/or via chewingmaterial. As a result, conventional techniques are unreliable andinconsistent because people are not uniformly trained to evaluatediverse materials.

Existing systems that can provide reliable and consistent resultsinvolve complex and cost-prohibitive machinery operated byhighly-skilled technicians. Those systems that can reliably test qualityand authenticity at the chemical or genetic level to determine purityand contaminants are non-portable and are implemented in a fewscientific laboratories or at manufacturing facilities. Mobilediagnostic testing systems can rely on expensive optical sensors andequipment (e.g. FTIR and NIR spectrophotometers), require adherence ofcostly and destructive optical signatures to materials, and requiretechnical experts to develop databases and to operate. As a result,reliable, affordable, and mobile techniques are unavailable tolaypeople. Consequently, for example, herbs sourced in developingcountries and shipped to the U.S. may not have been reliably tested forquality or authenticity due to a lack of affordable methods.Accordingly, a need exists for a cost-effective and scalable techniquefor ascertaining attributes of certain materials and without trainedexperts.

SUMMARY

The disclosed embodiments include at least one system, method, andapparatus for ascertaining attributes of sample material. A methodincludes mounting a handheld mobile device (e.g., smartphone) onto anexternal surface of a scanning device to capture images of samplesplaced on an internal surface of the scanning device. The capturedimages are communicated over a communications network to a service(e.g., cloud-based service) that can detect features in the capturedimages in accordance with artificial intelligence techniques todetermine the attributes of the sample material (e.g., includingimpurities). The attributes of the sample material can be certified andcommunicated for display on the smartphone and/or shared with interestedthird-parties.

A portable scanning device includes an external mounting structure forholding a handheld mobile device that includes a camera such that thecamera is positioned to capture multiple images of multiple samples thatare each placed on an internal surface of the portable scanning deviceto determine an attribute of each of the samples. The portable scanningdevice further includes an internal chamber including the internalsurface on which a particular sample is placed for capturing aparticular image of the particular sample with the camera such that anoptical distance from the camera to the internal surface and lightradiated by a light source within the internal chamber enable capturingof the images of the samples to determine the attribute of each of thesamples.

In some embodiments, the light source is a first light source of thehandheld mobile device and/or a second light source integrated in theportable scanning device.

In some embodiments, the internal surface is adjustable to change afocal distance between the camera and the internal surface.

In some embodiments, the attribute is at least one of authenticity,purity, or quality of each of the samples.

This Summary is provided to introduce a selection of concepts in asimplified form that are further explained in the Detailed Description.This Summary is not intended to identify key features or essentialfeatures of the claimed subject matter, nor is it intended to be used tolimit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a scanning device in a closed-door configurationthat can capture images of sample material contained therein.

FIG. 1B illustrates the scanning device of FIG. 1A in an opened-doorconfiguration.

FIG. 1C illustrates a chamber of the scanning device of FIG. 1A.

FIG. 2A illustrates a perspective view of another embodiment of ascanning device in a closed-door configuration.

FIG. 2B illustrates a top-down view of the scanning device of FIG. 2A.

FIG. 2C illustrates the scanning device of FIG. 2A in a closed-doorconfiguration with a handheld mobile device mounted thereon to captureimages of sample material contained in a chamber of the scanning device.

FIG. 3 illustrates another embodiment of a scanning device in aclosed-door configuration with a built-in touchscreen to control abuilt-in camera to capture images of sample material contained in achamber of the scanning device.

FIG. 4 illustrates a shelf with different trays thereon for samplematerials.

FIG. 5 is a block diagram that illustrates a system operable toascertain attributes of sample material based on image data.

FIG. 6 is a flowchart that illustrates a method performed by a scanningdevice to ascertain an attribute of sample material.

FIG. 7 is a flowchart that illustrates a method performed by a systemincluding a scanning device to ascertain an attribute of samplematerial.

FIG. 8 is a block diagram that illustrates a processing device operableto implement at least some aspects of the disclosed technology.

DETAILED DESCRIPTION

The embodiments set forth below represent the necessary information toenable those skilled in the art to practice the embodiments, andillustrate the best mode of practicing the embodiments. Upon reading thefollowing description in light of the accompanying figures, thoseskilled in the art will understand the concepts of the disclosure andwill recognize applications of these concepts that are not particularlyaddressed here. It should be understood that these concepts andapplications fall within the scope of the disclosure and theaccompanying claims.

The purpose of terminology used herein is only for describingembodiments and is not intended to limit the scope of the disclosure.Where context permits, words using the singular or plural form may alsoinclude the plural or singular form, respectively.

As used herein, unless specifically stated otherwise, terms such as“processing,” “computing,” “calculating,” “determining,” “displaying,”“generating,” or the like, refer to actions and processes of a computeror similar electronic computing device that manipulates and transformsdata represented as physical (electronic) quantities within thecomputer's memory or registers into other data similarly represented asphysical quantities within the computer's memory, registers, or othersuch storage medium, transmission, or display devices.

As used herein, terms such as “connected,” “coupled,” or the like, referto any connection or coupling, either direct or indirect, between two ormore elements. The coupling or connection between the elements can bephysical, logical, or a combination thereof.

The various materials (e.g., plant material) that are sourced and usedthroughout developed and developing countries are typicallypre-processed (e.g., dried, chopped, ground) before being traded incommerce. A processed material is typically mixed with other materialsor substances (e.g., contaminants). Given the variety of materials foundthroughout the world and complexity of supply chains, ascertainingattributes such as authenticity, purity, and quality of materials isexceedingly important.

A conventional technique for ascertaining attributes of materialsincludes manual assessments by a skilled technician. This results inunreliable and inconsistent outcomes because different people assessplant materials in different ways. Sophisticated techniques that involveadvanced technologies such as chemical, genetic, and other forms ofanalytical analysis provide consistent outcomes but are cost-prohibitiveand accessible to only a small fraction of the entire commercial market.

The disclosed embodiments overcome these drawbacks with a standalone ordistributed system that can utilize relatively inexpensive scanningdevices that are distributed across the entire commercial market touniformly ascertain attributes of widely distributed materials. Thescanning device has a minimum number of mechanical and/or electroniccomponents to stage samples of plant materials for capturing consistentimages. The scanning device is not limited to ascertaining attributes ofplant materials. Instead, the scanning device can analyze anything thatfits in its chamber and that has at least one visual characteristic(e.g., color, size, shape, or texture) that can be detected by thecamera but may be undetectable by an unaided human eye. It can analyzebiological and/or non-biological materials such as finished foods andsupplements and their ingredients, meats, fish, seafood, oils,beverages, cosmetics, gems, soil, etc.

In some embodiments, a handheld mobile device (e.g., mobile phone) ismounted on the scanning device to capture images of a sample containedwithin the scanning device. The mobile phone can be communicativelycoupled to a service over a cellular or computer network. The disclosedembodiments can take advantage of the ubiquity of mobile phones indeveloped and developing countries to enable a process for reliablyascertaining the quality and authenticity of plant materials in acost-effective manner. The backend service can be centralized ordistributed to collect images of various materials. The images can beprocessed with an artificial intelligence system of the service todetermine the quality and authenticity of the materials.

In some embodiments, a mobile application of the service allows users toascertain the attributes of materials in commerce by using smartphones.The images captured by a smartphone are uploaded into a network portalon either the smartphone or a personal computer. To ensure consistencyin image quality, the scanning device is designed for using a variety ofmobile phones to capture images of materials that can be compared fortraining and analysis. That is, a variety of mobile phones can becalibrated to use the scanning device.

In some embodiments, image data is transferred via an applicationprogramming interface (API) to a cloud-based image recognition softwareprogram (e.g. Google Cloud Vision), which uses computer vision/machinelearning to detect and classify features in the image data. Images thatare used to develop an authentication database for ascertaining theattributes of materials are obtained with smartphones mounted onscanning devices that capture images of samples of materials that havebeen independently verified via any number of known methods (e.g.,morphology, chemistry, and/or DNA sequencing).

The results that are output by the service are returned through an APIto a local user interface of the handheld mobile device or computer. Insome embodiments, a user can view resulting statistics and trends, andgenerate and share certificates of analyses. In some embodiments theimages, sample information, and results are returned through an API toexternal software (e.g. supply-chain management or blockchain) forpersistent storage. The disclosed embodiments include applications forauthenticating plant and animal species, detecting adulteration andfilth, assessing quality and manufacturing specifications, and trackingspecific lots or samples through a supply chain. Further, the disclosedembodiments can be validated so that product suppliers and manufacturerscan use it to comply with internal quality control specifications orother governmental regulations, such as the US Food and DrugAdministration's current Good Manufacturing Practices.

At least some of the disclosed embodiments are the first knownapplication of its kind designed for authentication and quality controlevaluation of plant materials (e.g., herbs, spices) in theircommercially traded forms (e.g., processed) such as dried, chopped, andpowdered. Examples of existing technologies for plant speciesidentification include PLANTSNAP, LIKETHATGARDEN, LEAFSNAP,FLOWERCHECKER, PLANTIFIER, NATUREGATE, AND IPFLANZEN. These technologiesare designed to identify species based on a whole living plant, leaves,or flowers, not the commercially traded forms of plant materials.Further, images used to generate the reference databases are usually notverified by independent experts, or they are extracted from GOOGLE IMAGEsearches. In contrast, the disclosed embodiments can use verifiedsamples for reference images contained in the database.

Moreover, the disclosed embodiments uniquely provide a scanning deviceto ensure that the reference and test images are uniform. This allowsthe disclosed technology to detect subtle differences between samplesthat are not possible without taking images in a controlled environment,including controlled lighting, background color, sample placement, andfocal distances.

The scanning device is portable, inexpensive, and designed forascertaining the attributes of different types of materials. To thisend, embodiments include a small and lightweight box composed of durablewhite, opaque, PVC, or other similar materials. The scanning device andassociated trays have several advantageous features. For example, thescanning device eliminates external light, provide consistent internallighting, consistent focal distance and background color, provides forconsistent amounts of material, and ensures that sample materials are inthe same position in a captured image so that software can consistentlycrop the captured images. The shelf distance to the handheld mobiledevice is adjustable to have the closest focal distance, to take clearhigh-quality images by using most smartphones. Moreover, a lower shelfallows for capturing images of larger-sized materials. In someinstances, the shelf has a marker (e.g., a logo) printed on its surfaceto cause the camera to autofocus and for calibration. The marker can beessential for analyzing powdered, light-colored, or highly-processedmaterials because a camera cannot readily autofocus on these materials.

An embodiment includes a five-sided box with a hinged door on the bottomor side edge of the box. The box can have an approximate size of 200 mm(length)×150 (width)×125 (height). An internal chamber of the box canhave removable lights (e.g., white, colored, incandescent, LED,ultraviolet (UV) or infrared (IR) light that are battery-powered andindependently operable. A smartphone can be placed on the top of the boxwith its camera lens positioned over a one-inch hole into the chamber.The box has four legs, which are unnecessary if a knob or pull is notrequired to open the door. For example, the box can have a mechanism topress and release the door, or an indent, notch or handle on the side toopen it. The chamber can hold shelf(s) for tray(s) that havecompartments of different sizes and shapes for different materials. Forexample, whole objects or less-processed materials go on a largercompartment of a tray, small objects can sit on smaller compartments,and liquids or powders can be held in a small square compartment of atray offset from the center. The camera lens can face directly insidethe box to capture images by switching the smartphone to camera mode.The box does not require a special lens, interface, or optical deviceattached to the camera. Lastly, the image data can be uploaded to acloud-based software through a web browser or native app on thesmartphone for processing.

For example, FIGS. 1A through 1C illustrate an embodiment of a scanningdevice 100. FIG. 1A illustrates the scanning device 100 in a closed-doorconfiguration. As illustrated, the scanning device is a small, portable,and lightweight box that measures about 6″×6″×8″. The scanning device100 has a chamber (not shown) that functions as a controlled environmentfor capturing images of sample materials. The scanning device 100includes an openable member 102 (e.g., a door) that enables access tothe chamber. A shelf (not shown) inside the chamber can receive a traywith sample material. As illustrated, the openable member 102 isattached to scanning device 100 with hinges 104-1 and 104-2, and ahandle 106 is used to open the openable member 102. The ambient light ofan external environment is blocked from the chamber when the openablemember is sealed closed. An exterior surface 108 of the scanning device100 receives a handheld mobile device 110, which includes a cameradevice. The handheld mobile device 110 is secured to the exteriorsurface 108 with one or more positioners 112-1 through 112-5 that areshaped like bumps. An opening 114 through the exterior surface 108 tothe chamber enables the handheld mobile device 110 to capture images ofsample material when its camera lens is positioned over the opening 114.

FIG. 1B illustrates the scanning device 100 in an opened-doorconfiguration. The chamber 116 can define level(s) for shelf(s) atpredefined distances from the opening 114 (not shown) through theexterior surface 108. The shelf(s) are adjustable to change a focaldistance to the camera. The floor 118 of the chamber 116 is furthestfrom the opening 114. The chamber 116 has support structures 120-1,120-2, 122-1, 122-2, 124-1, and 124-2 that can support a removable shelf126. For example, the level formed by the support structures 120-1 and120-2 can support the shelf 126 at a predefined distance to the opening114. Another level formed by the support structures 122-1 and 122-2support the shelf 126 at another predefined distance, closer to theopening 114 compared to the level formed by the support structures 120-1and 120-2. The support structures 124-1 and 124-2 can support the shelf126 at a closest predefined distance to the opening 114 compared to theother predefined distances.

The scanning device 100 can use a light source of the handheld mobiledevice 110 to illuminate the sample material in the chamber 116. Forexample, the opening may be sufficiently large to allow light from asource of the handheld mobile device 110 to illuminate the samplematerial in the chamber 116. In some embodiments, the scanning device100 includes a light source 128 that can radiate light on the samplematerial when disposed on the shelf 126. Examples of the light includesincandescent, LED, white, colored, ultraviolet (UV), or infrared (IR)light. As shown, the light source 128 is positioned on the ceiling ofthe chamber 116, has a circular shape, is removable, and faces the floor118 of the chamber 116. The camera device of the handheld mobile device110 faces the shelf 126 such that the light source 128 illuminates anarea including the field-of-view of the camera.

FIG. 1C illustrates the chamber 116 with a combination of two or moreremovable light sources. For example, the light sources can be attachedto the surfaces of the chamber 116 or inside surface of the door (notshown) by magnets. As shown, the down-facing light source 134 ispositioned on the ceiling of the chamber 116, has an elongated shape, isremovable, and faces the floor 118 of the chamber 116. Theforward-facing light source 136 is positioned on the rear wall of thechamber 116, has a rectangular shape, is removable and faces the door(not shown) of the scanning device 100. Further, the forward-facinglight source 136 is positioned to provide different amounts of lightdepending on the level at which the sample material is located. Forexample, the forward-facing light source 136 radiates more light at thelevel furthest from the floor 118 (closest to the opening 114 (notshown). The shelf 126 includes a marker 130 that enables the camera ofthe handheld mobile device 110 to autofocus on the shelf 126. The floor118 also includes a marker 130-2 that enables the camera of the handheldmobile device 110 to autofocus on the floor 118.

The removable trays of the scanning device 100 can hold sample material,and the tray is placed on the shelf 126. For example, the main tray 132can include a marker 130-3 that enables the camera of the handheldmobile device 110 to autofocus on the surface of the main tray 132. Thescanning device 100 can utilize trays of different shapes and sizes thatfacilitate ascertaining attributes of different sample materials. Thetrays are washable, reusable, and can have a matte surface to reducereflection. In most cases, the trays are white but could be black orother colors.

In one example, the main tray 132 can hold about 60 ml (¼ cup) ofprocessed material across about a 4″ by 5″ area, hold a single object,or hold multiple small objects. The removable trays may bebigger/smaller trays depending on the shape and design of a chamber. Asshown, the main tray has a raised lip around a rectangle in the centerthat holds the sample material. The area is optimized so that the cameraof the handheld mobile device 110 takes a picture of the entire areawithout having to crop the edges of the captured image. Hence, theentire image can be analyzed to ascertain an attribute of the samplematerial.

FIG. 4 illustrates specialty trays that can hold different forms oramounts of sample materials. The specialty tray 402 is a small rectanglein the center or offset to hold smaller amounts of materials. Thisdesign can be optimized for 10 ml (2 teaspoons) of powder material orliquid and fit standard disposable weigh boats. The specialty tray 402has a raised lip around the edges to put material on the tray. Thespecialty tray 404 is a flat square and/or has small dot on the centerto place a relatively small object. The square for liquids is placed inan area on the tray to avoid light reflection or shadows from the holeon top. The image processing software can crop out areas of the traythat were captured in an image of the sample material.

Therefore, the four different level positions of the chamber 116 canhold the tray, depending on the size of the sample material and desiredfocal length from the camera. Once the sample material is placed at alevel and the light source 128 is switched on, the openable member 102is closed to seal the chamber from external light. The opening 114 onthe top of the scanning device 100 then allows the camera to capture animage of the inside of the chamber 116 that contains the samplematerial. In some embodiments, another lens, such as a magnifying ormicroscopic lens is attached to the camera lens of the camera to augmentthe function of the camera.

The scanning device is not limited to the embodiments illustrated inFIGS. 1A through 1C. For example, a scanning device can have a carryinghandle that folds in/out. The positioners 112 on the top of the scanningdevice can be adjustable to position different types of handheld mobiledevices (e.g., smartphones, tablet computers). In some embodiments, oneor more of the positioners 112 can be eliminated or removed. The scannerdevice can have integrated lights that run off a removable battery packor plug into a wall outlet. In some embodiments, the scanning device hasan integrated, rechargeable battery pack, an on/off switch/button, ahinged door or pull-out compartment, etc. The light sources can includewhite or colored LED or incandescent lights, ultraviolet (UV) orinfrared (IR) lights. Moreover, an internal light source may betriggered to illuminate only when the door is closed, and/or the tray isinserted. In some embodiments, rather than having small legs, thescanning device has a larger base with a pull mechanism for an openablemember.

FIGS. 2A through 2C illustrate another embodiment of a scanning device200. Specifically, FIG. 2A illustrates a perspective view of thescanning device 200 with rounded edges in a closed-door configurationand FIG. 2B illustrates a top-down view of the scanning device 200. Thescanning device 200 is portable and has a handle 202 for a user to carrythe scanning device 200. The scanning device 200 has a chamber (notshown) that provides a controlled environment for capturing images ofsample materials contained in the chamber. A push/pull openable member204 allows a user to access the chamber. Similar to the previouslydescribed embodiments, sample material is placed on a tray that isplaced on a removable shelf inside the chamber. As illustrated, a usercan push a button 206 to open/close the openable member 204 toplace/remove sample material. The light of an external environment isblocked from the chamber when the openable member 204 is closed.

An exterior surface 208 of the scanning device 200 can receive ahandheld mobile device that includes a camera. The handheld mobiledevice is secured to the exterior surface 208 with one or moreadjustable positioners 210-1 through 210-3. The positioners 210 areoptional features that, in some embodiments, are unnecessary. Forexample, surface 218 can include markers to guide the user forpositioning the handheld mobile device. An opening 212 through theexterior surface 208 to the chamber enables the camera of the handheldmobile device to capture the image of the sample material in thechamber. FIG. 2B also shows markers 214-1 through 214-4 that aid theuser in positioning a handheld mobile device so that its camera is overthe opening 212. FIG. 2C illustrates the scanning device 200 in aclosed-door configuration with the handheld mobile device 216 mounted onthe exterior surface 208 to capture an image of sample materialcontained in the chamber. As shown, the handheld mobile device 216 ispositioned on the exterior surface 208 such that the camera of thehandheld mobile device 216 is positioned over the opening and faces theshelf in the chamber.

In another embodiment, a camera is built-in a scanning device to captureimages of samples without needing a separate handheld mobile device. Thescanning device can also have a built-in interface such as a touchscreenon a top surface where the handheld mobile device normally sits. Thescanning device itself may have a wireless interface to connect toWi-Fi, cell service, and/or can connect to a handheld mobile device via,for example, BLUETOOTH. The scanning device can have a screen thatallows one to see sample material inside the chamber and allow a user tocontrol when to capture images and send image data to a handheld mobiledevice or upload to a remote computer system. In another embodiment, thebuilt-in camera can be controlled by the handheld mobile device. Thescanning device can identify a tray and/or type of material in thechamber to automatically select a correct cropping function.

FIG. 3 illustrates an embodiment of a scanning device 300 in aclosed-door configuration that includes a built-in touchscreen 310 and abuilt-in camera device (not shown) to capture images of sample materialcontained within a chamber (not shown). A user can control the built-incamera with the touchscreen 310. In some embodiments, the scanningdevice 300 is a standalone device that does not require a separateelectronic device and/or network connectively to analyze samplematerial. The disclosed scanning devices can be part of a system thatincludes a server configured to process image data that is received overa network from a handheld mobile device or the scanner device. A remoteserver can determine an attribute of sample material based on theprocessed image data.

FIG. 5 is a block diagram of a system that can implement at least someaspects of the disclosed technology. The system 500 includes componentssuch as cloud-based resources 502, one or more service provider servers504 that use the cloud-based resources 502 to ascertain attributes ofmaterials by the mobile phone 506 mounted on the scanning device 508,which are interconnected over a network 510, such as the Internet, tofacilitate accurate testing of sample material.

The network 510 can include any combination of private, public, wired,or wireless portions. Data communicated over the network 510 may beencrypted or unencrypted at various locations or along differentportions of the network 510. Each component of the system 500 mayinclude combinations of hardware and/or software to process data,perform functions, communicate over the network 510, etc. For example,any component of the system 500 may include a processor, memory orstorage, a network transceiver, a display, an operating system, andapplication software (e.g., for providing a user portal), etc. Othercomponents, hardware, and/or software included in the system 500 arewell known to persons skilled in the art and, as such, are not shown ordiscussed herein.

The cloud-based resources 502 can provide access to a shared pool ofconfigurable computing resources including servers, storage,applications, software platforms, networks, services, etc. Thecloud-based resources 502 are accessible by the service provider servers504 to offer resources to the mobile phone 506, which is mountable onthe scanning device 508. The service provider servers 504 may includeany number of computing devices that provide applications for servicesthat allow users to ascertain the quality and authenticity of plantmaterial. Although shown separate from the cloud-based resources 502,the service provider servers 504 may be part of the cloud-basedresources 502.

The cloud-based resources 502 can facilitate processing image data ofsamples captured by the mobile phone 506 mounted on the scanning device508. For example, the service provider servers 504 can train a machinelearning model and implement that machine learning model to ascertainthe quality and authenticity of plant material. The analysis may involveanalyzing the physical attributes of plant material captured in animage.

The mobile phone 506 is operated by a user that interacts with thesystem 500. An example of the mobile phone 506 is a smartphone (e.g.,APPLE (PHONE, SAMSUNG GALAXY), or any other handheld mobile device witha camera that is capable of being calibrated for capturing reliableimages of samples to enable ascertaining attributes of a captured imageof a sample material. The mobile phone 506 is also capable ofcommunicatively coupling with the service provider servers 504 over thenetwork 510. In some embodiments, any images of sample material capturedat the scanning device can be processed locally at the scanning devicewith local hardware and software that is loaded at the handheld mobiledevice 506 or some other local computing device. In other words, atleast some of the functions performed by the cloud-based resources 502and/or the service provider 504 can be performed locally at the scanningdevice 508.

The disclosure is not limited to a smartphone mounted on a separatescanning device. Examples of other suitable handheld mobile devices thatcan be mounted on the scanning device 508 include laptop computers(e.g., APPLE MACBOOK, LENOVO 440), tablet computers (e.g., APPLE IPAD,SAMSUNG NOTE, MICROSOFT SURFACE), or any other handheld mobile devicethat has an adequate camera and capabilities to communicate over thenetwork 510. In some embodiments, the scanning device 508 is aspecialized device that has the components of the mobile phone 506necessary to practice the disclosed embodiments.

In some embodiments, the service provider servers 504 provide oradminister a user interface (e.g., website, app) accessible from themobile phone 506. The user interface may include menu items forselecting image capture and/or processing operations and to presentanalytics about the quality and authenticity of plant material. The userinterface may also provide certificates of authenticity that can beshared with interested third-parties.

To provide reliable results regarding the quality, purity, orauthenticity of sample material, the disclosed embodiments can implementartificial intelligence techniques based on a broad range of imagescollected from diverse sources, and images of materials that have beenauthenticated by other generally accepted techniques such asmorphological, chemical, and/or genetic analysis. In one example, thedisclosed embodiments implement computer vision/machine learning (CV/ML)technology to ascertain the attributes of sample material. Specifically,users can upload images of sample material with their mobile phones to aservice that is remotely located from the locations where images werecaptured. The users can receive results from the service on their samemobile phones, a tablet computer or on any other computing device.

The service can be built on top of a unified platform. Hence, thedisclosed architecture gives a broad range of customers access to aservice by using mobile phones or other devices (e.g., tablet computer,personal computer) and networks that are ubiquitous in even remote partsof the world and may only require access to a relatively inexpensivescanning device to normalize the images of the sample materials forreliable, consistent, and trusted results. For example, the disclosedsolution can be deployed with cloud resources to take full advantage ofthe cloud's flexibility and scalability. The solutions and cloudresource management are both provided via a simple user interface. Thisallows administrators to allocate resources as needed, and to start/stopservers at a chosen schedule. The combination of unique computer visiontechnology and scalable platform on the cloud allows for the rapiddevelopment of accurate and robust solutions to enhance anauthentication process.

The disclosed CV/ML technology is trained with authenticated images ofdiverse materials that have been pre-processed according to acceptablecommercial practices. The training images can include combinations ofmaterials, impurities, and contaminants for detection in subsequentsamples. Hence, the service combines computer vision and deep learningtechnology on a scalable platform to bring affordable and uniquecapabilities to users throughout the world. In some embodiments, thedisclosed technology implements a variety of image recognitionalgorithms that combine both image matching with deep learning. Thiscombination allows the algorithms to complement each other in order tomaximize performance and accuracy.

In some embodiment, the service defines metadata, which includesattributes used to detect and identify material and impurities. In someembodiments, the authentication service is continuously trained bycapturing training images obtained from various sources and that includematerial that has been authenticated in accordance with traditionaltechniques. The training images can be uploaded by a variety of means toextract features that are labeled and stored as labeled features in adatabase. Examples of labeled features include species, plant or animalpart, or a variety of physical properties such as colors, dimensions,densities, etc. In some examples, objects such as rocks, sticks, androdent excreta are labeled. In some embodiments, the labeling of imagesand features in a training set is done automatically with detectionmethods and/or manually with skilled workers to provide a uniform andconsistent assessment of tested material.

In some embodiments, the training of the authentication service involvessetting-up training parameters that are continuously adjusted to controlthe efficiency and accuracy of processing. For example, a training jobcan be launched periodically based on images of authenticated plantmaterials to routinely update and adjust an authentication database. Thedatabase could be tested and/or recalibrated for accuracy byperiodically submitting images of authenticated plant materials of knownquality and impurities.

As such, the service can detect a range of materials and impurities tosuggest relevant labels for recognizable features. A combination ofrecognizable features detected in an image can be used to identify theattributes of sample materials. The service can be deployed on a varietyof networks including servers that are located in a centralized locationor in a decentralized architecture such as a blockchain network thatensures the reliability of results with sophisticated fault tolerance.In some embodiments, a cluster of servers is configured to run and scalethe service as needed. The service could also include an API integrationfor a variety of applications to further increase the usability of theservice.

In some embodiments, the service can implement an artificialintelligence technique that follows vision processing of conventionalskilled workers but in a way that ensures uniformity for reliablyaccurate results. For example, a convolutional neural network (CNN)could emulate the response of an individual neuron to visual stimuli,where each convolutional neuron processes data for its receptive field.Although fully connected feedforward neural networks can learn featuresas well as classify data, it is not necessarily practical to apply thisarchitecture to images because a very large number of neurons would benecessary due to the very large input sizes associated with images,where each pixel is a relevant variable. The convolution operation of aCNN solves this problem because it reduces the number of freeparameters. For example, regardless of image size, tiling regions ofsize 5×5, each with the same shared weights, requires only 25 learnableparameters. In this way, a CNN can resolve the problems that occur whentraining traditional neural networks with many layers by usingbackpropagation. As such, a CNN can reliably find patterns in images toascertain the attributes of sample materials.

FIG. 6 is a flowchart that illustrates a method performed by a scanningdevice to ascertain an attribute of sample material. In step 602, thehandheld mobile device captures an image of a sample material containedin an enclosed chamber of a portable scanning device. The image iscaptured by a camera of a mobile device when disposed on an exteriorsurface of the portable scanning device.

In step 604, the handheld mobile device can communicate image dataindicative to a remote computer system. The remote computer systemanalyzes the image data to ascertain one or more attributes of thesample material. The remote computer system then returns results overthe network to the handheld mobile device.

In step 606, the handheld mobile device itself can ascertain anattribute of sample material based on image data. In some embodiments,the sample material is a plant-based material. The attribute of theplant-based material is ascertained in accordance with machine learningtechniques as described elsewhere in this description. In someembodiments, the attribute of the sample material is ascertained bycomparing the captured image to image data of multiple sample materials.In some embodiments, the attribute is ascertained by comparing the imagedata to images of authenticated images including other images of thesame sample.

FIG. 7 is a flowchart that illustrates a method performed by a systemincluding a scanning device to ascertain an attribute of samplematerial. In step 702, a service obtains, over a communications network,image data obtained with a camera of a handheld mobile device mounted ona scanning device. In some embodiments, the method is performed at leastin part by a cloud-based service that is communicatively coupled to thehandheld mobile device over the communications network.

In step 704, the service determines one or more attributes of the samplebased on a combination of visual features detected in the captured imageof the sample. In some embodiments, the sample includes at least oneplant material and a non-plant contaminant. In some embodiments, thequality, purity, or authenticity of the plant material is determined inaccordance with machine learning techniques. In some embodiments,determining the quality, purity, or authenticity of the sample includesdetecting the visual features in the captured image of the sample, andidentifying contents of the sample based on the combination of detectedvisual features. In some embodiments, the entire image is classified, orsome, part, or a portion of the image is classified (e.g., a croppedimage). In some embodiments, the system can detect specified objects andcan count a quantity of the specified object.

In step 706, the service communicates the ascertained attributes of thesample material over the communications network to the handheld mobiledevice. In optional step 708, the service can cause the handheld mobiledevice to display instructions and/or automatically adjust the camera tocapture another image of the sample material or to adjust and/orvalidate an ascertained attribute.

FIG. 8 is a block diagram that illustrates a processing device 800(e.g., scanning device or service server) operable to implement thedisclosed technology. As shown, the processing device 800 includes a bus802 that is operable to transfer data between hardware and/or softwarecomponents. These components include a control 804 (e.g., processingsystem), a network interface 806, an input/output (I/O) system 808, anda clock system 810. The processing device 800 may include othercomponents that are not shown nor further discussed for the sake ofbrevity. One of ordinary skill in the art will understand any hardwareand software that is included but not shown in FIG. 8.

The control 804 includes one or more processors 812 (e.g., centralprocessing units (CPUs)), application-specific integrated circuits(ASICs), and/or field-programmable gate arrays (FPGAs), and memory 814(which may include software 816). For example, the memory 814 mayinclude volatile memory, such as random-access memory (RAM) and/ornon-volatile memory, such as read-only memory (ROM). The memory 814 canbe local, remote, or distributed.

A software program (e.g., software 816), when referred to as“implemented in a computer-readable storage medium,” includescomputer-readable instructions stored in the memory (e.g., memory 814).A processor (e.g., processors 812) is “configured to execute a softwareprogram” when at least one value associated with the software program isstored in a register that is readable by the processor. In someembodiments, routines executed to implement the disclosed embodimentsmay be implemented as part of operating system (OS) software (e.g.,MICROSOFT WINDOWS, LINUX) or a specific software application, component,program, object, module, or sequence of instructions referred to as“computer programs.”

As such, computer programs typically comprise one or more instructionsset at various times in various memory devices of a computer (e.g.,processing device 800), which, when read and executed by at least oneprocessor (e.g., processor 812), will cause the computer to performoperations to execute features involving the various aspects of thedisclosed embodiments. In some embodiments, a carrier containing theaforementioned computer program product is provided. The carrier is oneof an electronic signal, an optical signal, a radio signal, or anon-transitory computer-readable storage medium (e.g., memory 814).

The network interface 806 may include a modem or other interfaces (notshown) for coupling the processing device 800 to other computers overthe network 510. The I/O system 808 may operate to control various I/Odevices, including peripheral devices such as a display system 820(e.g., a monitor or touch-sensitive display) and one or more inputdevices 822 (e.g., a keyboard and/or pointing device). Other I/O devices824 may include, for example, a disk drive, printer, scanning device, orthe like. Lastly, the clock system 810 controls a timer for use by thedisclosed embodiments.

Operation of a memory device (e.g., memory 814), such as a change instate from a binary one (1) to a binary zero (0) (or vice versa) maycomprise a visually perceptible physical change or transformation. Thetransformation may comprise a physical transformation of an article to adifferent state or thing. For example, a change in state may involveaccumulation and storage of charge or a release of stored charge.Likewise, a change of state may comprise a physical change ortransformation in magnetic orientation or a physical change ortransformation in molecular structure, such as a change from crystallineto amorphous or vice versa.

Aspects of the disclosed embodiments may be described in terms ofalgorithms and symbolic representations of operations on data bitsstored in memory. These algorithmic descriptions and symbolicrepresentations generally include a sequence of operations leading to adesired result. The operations require physical manipulations ofphysical quantities. Usually, though not necessarily, these quantitiestake the form of electric or magnetic signals that are capable of beingstored, transferred, combined, compared, and otherwise manipulated.Customarily, and for convenience, these signals are referred to as bits,values, elements, symbols, characters, terms, numbers, or the like.These and similar terms are associated with physical quantities and aremerely convenient labels applied to these quantities.

While embodiments have been described in the context of fullyfunctioning computers, those skilled in the art will appreciate that thevarious embodiments are capable of being distributed as a programproduct in a variety of forms and that the disclosure applies equally,regardless of the particular type of machine or computer-readable mediaused to actually effect the embodiments.

While the disclosure has been described in terms of several embodiments,those skilled in the art will recognize that the disclosure is notlimited to the embodiments described herein and can be practiced withmodifications and alterations within the spirit and scope of theinvention. Those skilled in the art will also recognize improvements tothe embodiments of the present disclosure. All such improvements areconsidered within the scope of the concepts disclosed herein. Thus, thedescription is to be regarded as illustrative instead of limiting.

I/We claim:
 1. A system comprising: a portable scanning deviceincluding: a chamber configured as a controlled environment forcapturing an image of a sample material contained in the chamber; anopenable member configured to enable access to the chamber, whereinambient light of an external environment is blocked from the chamberwhen the openable member is closed; an exterior surface configured toreceive a handheld mobile device that includes a camera device; a shelfinside the chamber and configured to receive the sample material; and anopening through the exterior surface to the chamber to enable the cameradevice of the handheld mobile device to capture the image of the samplematerial when the handheld mobile device is positioned on the exteriorsurface such that the camera device is positioned over the opening andfaces the shelf.
 2. The system of claim 1 further comprising: a serverconfigured to: process data of the image, wherein the data of the imageis received over a network from the handheld mobile device; anddetermine an attribute of the sample material based on the processeddata of the image.
 3. The system of claim 1 further comprising: asupport structure that defines a first level of a plurality of levelsfor the shelf, wherein each level is at a predefined distance from theopening.
 4. The system of claim 3, wherein a second level of the shelfis a surface of the chamber.
 5. The system of claim 1 furthercomprising: a light source configured to radiate light on the samplematerial when disposed on the shelf.
 6. The system of claim 5, whereinthe radiated light includes at least one of white or colored LED orincandescent light, ultraviolet (UV) light, or infrared (IR) light. 7.The system of claim 5, wherein the light source is a removable device.8. The system of claim 1, wherein the shelf comprises: a marker thatenables the mobile device to autofocus the camera device.
 9. The systemof claim 1 further comprising: a removable tray disposed on the shelf,wherein the removable tray is configured to hold the sample material.10. The system of claim 9, wherein the removable tray comprises: amarker that enables the handheld mobile device to automatically focusthe camera device.
 11. The system of claim 1 further comprising: aplurality of removable trays of different sizes, wherein each of theplurality of removable trays is configured to hold a different type ofsample material.
 12. The system of claim 1, wherein the exterior surfacecomprises: a plurality of positioners configured to orient the handheldmobile device for capturing the image of the sample material with thecamera device.
 13. The system of claim 12, wherein a positioner isadjustable to fit a plurality of handheld mobile devices of differentsizes.
 14. A method comprising: capturing an image of a sample materialcontained in an enclosed chamber of a portable scanning device, whereinthe image is captured by a camera device of a mobile device disposed onan exterior surface of the portable scanning device; and ascertaining anattribute of the sample material based on the image.
 15. The method ofclaim 14, wherein the sample material is a biological material.
 16. Themethod of claim 14, wherein the sample material is a plant-basedmaterial, and the attribute of the plant-based material is determined inaccordance with machine learning techniques.
 17. The method of claim 14,wherein ascertaining the attribute of the sample material comprises:comparing the image to data of another image of the sample material toverify the attribute.
 18. The method of claim 14, wherein ascertainingthe attribute of the sample material comprises: comparing the image todata of a plurality of images of authenticated samples.
 19. A portablescanning device comprising: a chamber configured as a controlledenvironment for capturing an image of a sample material contained in thechamber; an openable member configured to enable access to the chamberfor receiving the sample material, wherein ambient light of anenvironment external to the chamber is blocked from the chamber when theopenable member is closed; a platform inside the chamber configured toshelf the sample material for capturing the image of the samplematerial; and an imaging device configured to capture the image of thesample material when disposed on the platform inside the chamber. 20.The portable scanning device of claim 19 further comprising: atouchscreen integrated in an exterior surface of the scanning device,wherein the touchscreen enables a user to control of the imaging device.21. The portable scanning device of claim 19, wherein the imaging deviceis a camera integrated in a surface of the chamber and facing theplatform.
 22. A method comprising: obtaining, over a communicationsnetwork, an image of a sample captured with a camera of a handheldmobile device that is mounted on a scanning device configured as aplatform for uniformly capturing images of different samples fromdifferent sources; determining an attribute of the sample based on acombination visual features detected in the captured image of thesample; and communicating, over the communications network, anindication of the determined attribute of the sample for display on thehandheld mobile device.
 23. The method of claim 22, wherein the sampleincludes at least one plant material and a non-plant contaminant. 24.The method of claim 23, wherein the attribute of the plant material isdetermined in accordance with machine learning techniques.
 25. Themethod of claim 22, wherein the handheld mobile device is a smartphoneor tablet computer.
 26. The method of claim 22 further comprising:causing the handheld mobile device to adjust the camera and captureanother image of the sample to adjust or validate the determinedattribute of the sample.
 27. The method of claim 22 further comprising:causing the handheld mobile device to display instructions for adjustingthe scanning device and capture another image of the sample to update orvalidate the determined attribute of the sample.
 28. The method of claim22, wherein determining the attribute of the sample comprises: detectingthe visual features in the captured image of the sample; and identifyingcontents of the sample based on the combination of detected visualfeatures.
 29. The method of claim 22, wherein the method is performed bya cloud-based service communicatively coupled to the handheld mobiledevice over the communications network.